Distributed Energy Management Algorithm Research Articles

Stealthy Data Integrity Attack Against Consensus-Based Distributed Energy Management Algorithm

Stealthy Data Integrity Attack Against Consensus-Based Distributed Energy Management Algorithm

An IoT-fog-cloud consensus-based energy management algorithm of multi-agent smart energy hubs considering packet losses and uncertainty

—As a result of the increasing demand for different energies with technological development, and with the rapid development of energy hubs (EHs), issues such as computation speed, big data and information privacy, robustness, etc. are becoming increasingly vital. Also, the problem of communication packet loss leads to an incompatibility between the generated and consumed energy. In response to the above problems, this paper proposes an Internet of things-Fog-Cloud distributed consensus-based energy management algorithm to optimally solve the energy mismatch problem in an EH environment considering communication information loss and uncertainties of some EH elements. A MATLAB/Simulink-based model of a hypothetical EH system is modified to support real-time communication. Two case studies are applied to explain the performance and effectiveness of the proposed algorithm under different packet loss scenarios. The system's sensitivity was studied depending on the number of iterations and the uncertainties of the renewable energy sources, energy prices, and load demands. Also, a comparison was made between the results obtained by the proposed method and the optimization results using the genetic algorithm and particle swarm optimization techniques. The results prove the applicability and feasibility of the proposed method for the demand management system in EHs. The results show that the proposed algorithm decreases the daily system operating cost from 372.421 to 369.941 $ and the total CO2 emissions cost from 4848.5 to 4103.1 $. In addition, the daily energy mismatch between the generated and consumed values decreases from 579.85 to 0 kWh in the electrical system and from 435.14 to 0.073 kWhth in the heating system. Based on the previous results, the proposed algorithm of EHs contributes significantly to the development of sustainable cities.

Experimental and developed DC microgrid energy management integrated with battery energy storage based on multiple dynamic matrix model predictive control

This study presents the energy management and control strategy in the islanded DC microgrid structure in the presence of renewable energy sources (RES) and battery storage units (BU). The BU control structure is planned by considering the state of charge (SOC) indicator of each BU. The proposed model based on sequential distributed energy management and multiple dynamic matrix model predictive control algorithm (MDMMPC) is developed. The MDMMPC algorithm is implemented for power control and management by local controllers. The energy management strategy is formulated by considering generation prioritization and minimal communication based on primary and secondary control objectives. The simulation results have been analyzed in different scenarios such as power generation changes, load changes, disconnection between participating units in energy supply and battery discharge. Also, a hardware-in-the-loop (HIL) environment along with an experimental setup based on the Micro Lab box and dSPACE control desk (DS1202) is presented. In experimental environment, by creating suitable coordination between the converter's behavior and the ESS unit inertia, it not only reduces the undesirable converter's fluctuations but also the converter's behavior is associated with the least overshoot. Simplicity, rapidity, ease of operation, and distributed control scheme are the important features of the experimental structure.

Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization

This article proposes a fully distributed energy management algorithm for dc microgrids, resilient to different faults. Specifically, we employ distributed model-predictive control to deal with the uncertainty that characterizes the microgrid operation. The optimization problem is solved at each time step through a distributed optimization algorithm, which has three main advantages: 1) agents of the network require a small computational power; 2) local information is not shared among the network nodes, hence preserving a certain level of privacy; and 3) it is suitable for implementation in large-scale systems. The resilience property of the algorithm stems from additional constraints that are enforced in order to store in the system enough energy to sustain the microgrid in the case of utility grid or line fault. Simulation results show that the algorithm is suitable to schedule the operation of agents that are always connected to the microgrid (e.g., loads) as well as agents that may be connected and disconnected (e.g., electric vehicles).

A Switched Newton–Raphson-Based Distributed Energy Management Algorithm for Multienergy System Under Persistent DoS Attacks

The security and economy of multienergy systems (MESs) are directly threatened by the potential cyberattacks. It is of great importance to investigate the effects of cyberattacks, e.g., denial of service (DoS) attacks, on distributed energy management algorithms. To this end, this article focuses on exploring how the frequency and the time of duration of DoS attacks influence the behavior of Newton–Raphson-based distributed energy management (NRBDEM) algorithm for MES and in which condition the optimal operations can still be obtained. First, a switched NRBDEM algorithm is presented, which is composed of the normal operation mode and the attack mode. In the attack mode, the attackers are able to change the communication structure at will and make it unconnected to destroy the convergence of the switched NRBDEM algorithm. Then, by making use of automatons to generate the hybrid time domain, the switched NRBDEM algorithm is further modeled and formulated as a hybrid dynamical system, which provides a mathematical model for the subsequent convergence analysis. Therein, the generated hybrid time domain satisfies the average dwell-time constraint and time-ratio constraint to limit the persistent attacks. Furthermore, we analyze the restrained conditions for persistent attacks, under which the optimality and convergence of the switched NRBDEM algorithm can be guaranteed still. Finally, simulation results demonstrate the effectiveness of the proposed method. Note to Practitioners—The multienergy system (MES) is viewed as a typical cyber-physical system. Since it works in a networked environment, the convergence and optimality of the corresponding distributed energy management algorithms are easily destroyed by various cyberattacks, such as the DoS attack. Few noticeable studies have been documented to investigate the distributed energy management problem for MES under persistent DoS attacks. To address this issue, this article proposes a switched NRBDEM algorithm subject to persistent DoS attacks, which is made up of normal operation mode and attack mode. Moreover, sufficient conditions are derived for the tolerable frequency of attack and the time of duration. It is helpful for the practitioners to know in which conditions the global convergence and optimality can be maintained still. Several simulations are provided to verify the correctness of the proposed theoretical analysis results. Our future work will aim at analyzing the performance of different energy management algorithms under different cyberattacks.

Distributed Resilient Double-Gradient-Descent Based Energy Management Strategy for Multi-Energy System Under DoS Attacks

As a typical cyber-physical system, multi-energy system is easier to suffer from the denial of service (DoS) attacks, which brings huge risks for the effective execution of distributed energy management algorithms. To address this issue, this paper studies the collaborative energy management problem for multi-energy system under DoS attacks, where the optimal operation of each energy hub and the optimal energy trading path among energy hubs are taken into account simultaneously. To enhance the system robustness, a distributed resilient double-gradient-descent based energy management strategy is proposed, which is designed by establishing multiple control protocols. The proposed method is initialization-free and can reasonably utilize the estimation to compensate the missing information during attack periods, which possesses strong robustness and adaptability against the DoS attacks. Furthermore, based on theoretical analysis, it is proved that the proposed method can still converge to the global optimal solutions although the connectivity of the communication network is broken during attack periods. Finally, several case studies are provided to verify the effectiveness and correctness of the proposed method.

Distributed optimization for structured programs and its application to energy management in a building district

This paper deals with structured multi-agent optimization problems that involve coupled local and global decision variables. We propose an iterative distributed algorithm that explicitly accounts for this structure, and requires the agents to communicate only their tentative solutions for the global variables throughout iterations. Our approach extends to structured multi-agent optimization a proximal-based distributed methodology that has recently appeared in the literature. Privacy of local information is preserved and communication effort is reduced with respect to alternative distributed solutions where local and global optimization variables are grouped together and treated as a single decision vector. Multi-agent optimization problems with the considered structural properties appear in various contexts. In this paper, we apply our approach to energy management in a district where multiple buildings can communicate over a possibly time-varying network and aim at optimizing the use of shared and local resources. We illustrate the efficacy of the resulting distributed energy management algorithm by means of a detailed simulation study on a cooling problem.

Real-Time Bilevel Energy Management of Smart Residential Apartment Building

This article elucidates a real-time energy management strategy for a smart residential apartment building having nonidentical occupants at the dwelling units (DUs). The aim of the present article is to design a distributed energy management algorithm, which can optimize the real-time demand of the entire building against abruptly updated rooftop solar generation and real-time price (RTP) of energy. The proposed energy management strategy differentiates among the DUs by considering a new parameter named load criticality level, which is defined as the value imposed by the DU residents to their power consumption. The optimization portfolio is developed as a novel bilevel, stochastic, multiobjective optimization problem where the maximization of utility of the consumed power is considered simultaneously with the cost minimization. To this end, a virtual energy trading platform is designed in this article between central building management system and the DUs, where they interact with each other by following the directives of single-leader multifollower Stackelberg game. The solution strategy is proposed as a Lyapunov optimization, which needs only the current values of the uncertain parameters, such as load variation, renewable generation, and energy price, and do not require any knowledge about their probabilistic variation, to eliminate the complexities regarding time average stochastic equations. Strenuous simulation on real-time data of four DUs, it is proved that the proposed framework can track the abrupt change in RTP and solar generation efficiently. Comparing with two benchmark methods viz. centralized process and greedy algorithm, the superiority of the designed energy management portfolio is established.

A Resilient Consensus-Based Distributed Energy Management Algorithm Against Data Integrity Attacks

This paper addresses the vulnerability of consensus-based distributed energy scheduling algorithm to data integrity attacks. A reputation-based neighborhood-watch mechanism is introduced to detect the false information and achieve optimal operating point in the presence of misbehaving controllers. The reputation-based neighborhood watch algorithm has three major functions: 1) verifying the correctness of neighbors’ information based on two-hop shared information; 2) identifying the compromised controller based on reputation indexes; and 3) maintaining the correctness of local information estimation in the presence of false information. The effectiveness of the proposed method is illustrated through simulation analyses in the future renewable electric energy delivery and management system.

A Novel Data Integrity Attack on Consensus-Based Distributed Energy Management Algorithm Using Local Information

This paper introduces a novel data integrity attack on the well-developed consensus-based energy management algorithm. In particular, we show that by sending out elaborately falsified information during the consensus iterations, attackers could manipulate the system operating point and gain extra economic benefits. Meanwhile, the system-level and device-level constraints are still satisfied, e.g., the power generation and demand are balanced, and the operation of individual device respects physical constraints. This data integrity attack has two major features: First, attackers rely only on local information to complete the attack; neither additional information about system topology nor additional colluders are required; second, the attacking effect is accumulative, which enables attackers to choose to finish in either single or multiple iterations. By revealing such vulnerability of consensus-based applications to data integrity attack, this paper conveys the message that besides the efforts of designing novel distributed energy management algorithms to address the renewable energy integration challenges, it is equally important to protect the distributed energy management algorithms from possible malicious attacks to avoid potential economic losses. The proposed attack is illustrated in the Future Renewable Electric Energy Delivery and Management system.

Privacy-Preserving Consensus-Based Energy Management in Smart Grids

Privacy has become a big concern for consumers in electricity consumption activities, as privacy disclosure may cause losses to individuals. Since the information exchange and update in distributed energy management (DEM) of smart grids leaves eavesdroppers an opportunity to obtain the private information, it is worth studying privacy disclosure of DEM and design effective privacy-preserving schemes. In this paper, we investigate the privacy concern of a consensus-based DEM algorithm, where both generation units and responsive consumers cooperatively maximize the social welfare. First, we reveal that the private information of consumers including the electricity consumption and the sensitivity to the electricity price can be disclosed under traditional consensus-based DEM. Then, we propose a secret-function-based privacy-preserving algorithm to preserve the private information, where each node adds zero-sum and exponentially decaying noises to the original data for communications. It is assumed that local secret function can only be known by neighboring nodes. To relax this assumption, we propose a privacy-preserving algorithm, where each node utilizes real information for the state update and broadcasts the one with noise. We show that both of two proposed algorithms can preserve the privacy and the privacy degree is analyzed through $(\epsilon, \delta)$ -data-privacy. At the same time, the convergence and optimality of final solution are maintained. Extensive simulations demonstrate the effectiveness of proposed algorithms.

Distributed Energy Management in Smart Grid With Wind Power and Temporally Coupled Constraints

This paper investigates the problem of distributed energy management for both generation and demand sides in a smart grid by formulating the economic dispatch and demand response in a united framework. Our main contribution is to formulate a social welfare maximization problem for a more practical scenario by taking wind power, and temporally coupled constraints of the demands into account. The complexity lies in the nonquadratic cost function of wind power, the temporally coupled constraints of the demands, and the nonconvexity of the optimization problem. Meanwhile, a smart grid has to guarantee privacy and accommodates plug-and-play features. Aiming at these challenges, we first relax an equality constraint to obtain a new convex optimization problem. Because of the coupling in the constraint, the Lagrange duality method is then adopted to decompose the problem into subproblems for generators and demands, which are regarded as agents. As a result, each agent solves its subproblem by exchanging information with only neighbor agents, and coordinates with others using the global information discovered by a distributed finite-time consensus algorithm. We also prove the convergence and optimality of the proposed distributed energy management algorithm (DEMA). Finally, simulations are performed on the IEEE 39-bus system to illustrate the performance of our DEMA.

Resilient Distributed Energy Management Subject to Unexpected Misbehaving Generation Units

Distributed energy management algorithms are being developed for the smart grid to efficiently and economically allocate electric power among connected distributed generation units and loads. The use of such algorithms provides flexibility, robustness, and scalability, while it also increases the vulnerability of smart grid to unexpected faults and adversaries. The potential consequences of compromising the power system can be devastating to public safety and economy. Thus, it is important to maintain the acceptable performance of distributed energy management algorithms in a smart grid environment under malicious cyber-attacks. In this paper, a neighborhood-watch-based distributed energy management algorithm is proposed to guarantee the accurate control computation in solving the economic dispatch problem in the presence of compromised generation units. The proposed method achieves the system resilience by performing a reliable distributed control without a central coordinator and allowing all the well-behaving generation units to reach the optimal operating point asymptotically. The effectiveness of the proposed method is demonstrated through case studies under several different adversary scenarios.